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Researchers from UC Berkeley have developed a deep learning model that can automatically transfer your font style. The model is named multi-content generative adversarial network (MC-GAN). It consists of a stacked conditional generative adversarial network (cGAN) architecture to predict the coarse glyph shapes and an ornamentation network to predict color and texture of the final glyphs. As an example, it can generate a new title with the same style from a movie poster.
Courtesy of the researchers
This is not the first time deep learning has been used for style transfer. For example, convolutional neural networks (CNN) can be used for image style transfer (see https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf).
For more details about MC-GAN, please visit their blog at http://bair.berkeley.edu/blog/2018/03/13/mcgan/.
|Call for Nominations: Chair, Women in Signal Processing Committee and Chair, Young Professionals Committee||10 July 2020|
|ALASKA 2 Steganalysis Challenge is Open||13 July 2020|
|Nominations Open for 2020 SPS Awards||1 September 2020|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2020|
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